Trusted AI Starts With Trusted Data
Why Data Quality, Data Integrity and Control Evidence Matter.
A focused introduction to the difference between data quality and data integrity, why monitoring can appear stable while data has already failed, and why AI assurance depends on trusted data journeys.
Alternative Presentation Concepts
The same Episode 001 narrative presented using different production styles. These examples illustrate how DQIntegrity content can be adapted for executive briefings, conference presentations and visual storytelling.
What this episode covers
- Why data quality and data integrity are not the same thing
- How hidden data failures create false confidence
- Why monitoring effectiveness depends on proven data journeys
- Why AI assurance must start with data integrity proof
Working principle
“Data quality improves what you can see. Data integrity determines whether what you see can be trusted.”
This is the core distinction behind DQIntegrity’s work across completeness, correctness, monitoring integrity and control evidence.